The present invention relates to the field of data management and, more particularly, to autonomous intelligent content items.
Content managements systems are commonly used to provide library functions (e.g., storage, access, versioning) for a variety of content data. While conventional content management systems provide the structure for handling content data, these systems often require an increasingly larger amount of storage space to house not only the content data, but also the additional data generated as overhead.
As such, conventional content management systems tend to grow to unmanageable sizes. To address this problem, organizations often resort to using multiple content management systems, one for each category or grouping of content data. While this approach helps to alleviate performance issues related to large library sizes, this approach does not provide content consumers (e.g., users, business processes) with an easy means to locate content data; content consumers must know the exact location of the content data ahead of time or waste time searching through the various content management systems.
Further, conventional content management systems are built on the principle of centralized storage. Accessing content data from a conventional content management system takes a variable amount of time, depending on network conditions and how far the content consumer is from the centralized storage space. While this is not a problem for some users, this delay introduces performance inconsistencies to content consumers like business processes/services that function with time constraints.
Conventional content management systems are unable to remedy this situation. Identifying the performance problem is a manual and laborious process. Relocating the content data would alleviate some of the inconsistencies, but is not allowed for content data managed under centralized storage.